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An Implicit, Non-Verbal Measure of Belief Attribution to Robots

Published: 01 April 2020 Publication History

Abstract

Studies of mental state attribution to robots usually rely on verbal measures. However, verbal measures are sensitive to people's rationalizations, and the outcomes of such measures are not always reflected in a person's behavior. In light of these limitations, we present the first steps toward developing an alternative, non-verbal measure of belief attribution to robots. We report preliminary findings from a comparative study indicating that the two types of measures (verbal vs. non-verbal) are not always consistent. Notably, the divergence between the two measures was larger when the task of inferring the robot's belief was more difficult.

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Cited By

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  • (2024)Does the Robot Know It Is Being Distracted? Attitudinal and Behavioral Consequences of Second-Order Mental State Attribution in HRI2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN)10.1109/RO-MAN60168.2024.10731278(1134-1141)Online publication date: 26-Aug-2024
  • (2024)Anthropomorphism and Human–Robot InteractionThe Cambridge Handbook of the Law, Policy, and Regulation for Human–Robot Interaction10.1017/9781009386708.005(17-56)Online publication date: 7-Dec-2024
  • (2024)An Introduction to the Law, Policy, and Regulation for Human–Robot InteractionThe Cambridge Handbook of the Law, Policy, and Regulation for Human–Robot Interaction10.1017/9781009386708.003(1-170)Online publication date: 7-Dec-2024
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  1. An Implicit, Non-Verbal Measure of Belief Attribution to Robots

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      cover image ACM Conferences
      HRI '20: Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction
      March 2020
      702 pages
      ISBN:9781450370578
      DOI:10.1145/3371382
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Published: 01 April 2020

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      1. human-robot interaction
      2. mental state attribution
      3. methodology

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      View all
      • (2024)Does the Robot Know It Is Being Distracted? Attitudinal and Behavioral Consequences of Second-Order Mental State Attribution in HRI2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN)10.1109/RO-MAN60168.2024.10731278(1134-1141)Online publication date: 26-Aug-2024
      • (2024)Anthropomorphism and Human–Robot InteractionThe Cambridge Handbook of the Law, Policy, and Regulation for Human–Robot Interaction10.1017/9781009386708.005(17-56)Online publication date: 7-Dec-2024
      • (2024)An Introduction to the Law, Policy, and Regulation for Human–Robot InteractionThe Cambridge Handbook of the Law, Policy, and Regulation for Human–Robot Interaction10.1017/9781009386708.003(1-170)Online publication date: 7-Dec-2024
      • (2023)We Do Not Anthropomorphize a Robot Based Only on Its Cover: Context Matters too!Applied Sciences10.3390/app1315874313:15(8743)Online publication date: 28-Jul-2023
      • (2023)Dictator Game with a Robot in Children with Autism Spectrum Disorders: Sharing is Predicted by Positive Attributions Towards the AgentHuman and Artificial Rationalities10.1007/978-3-031-55245-8_20(296-322)Online publication date: 19-Sep-2023

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